

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
	import tensorflow as tf

	from transformers import TFBlipTextModel
	from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=12  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=32  ,      _lowercase=2  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=0.0_2  ,      _lowercase=0  ,      _lowercase=None  ,      )     -> int:
									a_						:						Dict 							= parent
									a_						:						Tuple 							= batch_size
									a_						:						List[str] 							= seq_length
									a_						:						str 							= is_training
									a_						:						Any 							= use_input_mask
									a_						:						Optional[Any] 							= use_labels
									a_						:						List[Any] 							= vocab_size
									a_						:						Tuple 							= hidden_size
									a_						:						Tuple 							= projection_dim
									a_						:						List[Any] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						List[str] 							= intermediate_size
									a_						:						Union[str, Any] 							= dropout
									a_						:						List[Any] 							= attention_dropout
									a_						:						Optional[Any] 							= max_position_embeddings
									a_						:						List[Any] 							= initializer_range
									a_						:						Optional[int] 							= scope
									a_						:						List[Any] 							= bos_token_id





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						List[Any] 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						List[Any] 							= None
									if self.use_input_mask:
												a_						:						str 							= random_attention_mask([self.batch_size, self.seq_length]    )

									if input_mask is not None:
												a_						:						Union[str, Any] 							= input_mask.numpy()
												a_							,					a_						:						Optional[Any] 							= input_mask.shape
												a_						:						str 							= np.random.randint(1  ,      seq_length - 1  ,      size=(batch_size,)    )
												for batch_idx, start_index in enumerate(_lowercase    ):
															a_						:						Optional[Any] 							= 1
															a_						:						Any 							= 0

									a_						:						Any 							= self.get_config()

									return config, input_ids, tf.convert_to_tensor(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									return BlipTextConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      projection_dim=self.projection_dim  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      dropout=self.dropout  ,      attention_dropout=self.attention_dropout  ,      max_position_embeddings=self.max_position_embeddings  ,      initializer_range=self.initializer_range  ,      bos_token_id=self.bos_token_id  ,      )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Union[str, Any]:
									a_						:						Tuple 							= TFBlipTextModel(config=_lowercase    )
									a_						:						int 							= model(_lowercase  ,      attention_mask=_lowercase  ,      training=_lowercase    )
									a_						:						Optional[int] 							= model(_lowercase  ,      training=_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )
									self.parent.assertEqual(result.pooler_output.shape  ,      (self.batch_size, self.hidden_size)    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						Optional[Any] 							= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict







@require_tf
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			(TFBlipTextModel,) if is_tf_available() else ()
						_A    :				List[str]       					=			False
						_A    :				Tuple       					=			False
						_A    :				Union[str, Any]       					=			False





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Any 							= BlipTextModelTester(self    )
									a_						:						Dict 							= ConfigTester(self  ,      config_class=_lowercase  ,      hidden_size=37    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Optional[int] 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									pass





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						@unittest.skip(reason="""Blip does not use inputs_embeds"""    )
						def 			UpperCamelCase__					(    self    )     -> int:
									pass





						@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass





						@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass





						@slow
						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a_						:						Union[str, Any] 							= TFBlipTextModel.from_pretrained(_lowercase    )
												self.assertIsNotNone(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase=True    )     -> Any:
									super().test_pt_tf_model_equivalence(allow_missing_keys=_lowercase    )





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


